ArticleScientific reports2021
Effect of data leakage in brain MRI classification using 2D convolutional neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer's Disease: A Preprocessing-Focused Pipeline.Journal of imaging · 2026Article
- Biomarker-Conditioned Vision Transformers with Deformable Biomarker Attention for Alzheimer's Disease Classification.Journal of imaging informatics in medicine · 2026Article
- Coarse-to-Fine Curriculum Transfer Learning Using RF-Derived Ultrasound Representations for Small-Data Breast Tumor Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Interpretable 2D Deep Learning for Alzheimer's Detection from sMRI: A Lightweight Residual CNN Approach with Comprehensive Preprocessing and Stratified Data Partitioning.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence, Deep Learning, and Computer Vision in Hysteroscopy: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.Human brain mapping · 2026Article
- Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.Brain and behavior · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Comment on Iacobescu et al. Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities.Journal of cardiovascular development and disease · 2026Article
- Ensemble deep learning architectures for detecting pulmonary tuberculosis in chest X-rays.Scientific reports · 2026Article
- Evolution of deep learning for structural magnetic resonance imaging analysis in Alzheimer's disease: from slice-level classification to brain structure modeling.Frontiers in human neuroscience · 2026Review
- Efficient attention-based Ghost-ResNet for brain tumor classification in magnetic resonance imaging (MRI).Frontiers in neuroscience · 2026Article
- Multimodal classification of mood disorders using structural MRI and polygenic risk scores: a machine learning ablation study in UK Biobank.Frontiers in psychiatry · 2026Article
- Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models.Computer methods and programs in biomedicine · 2025Article
- Using Deep Learning to Simultaneously Reduce Noise and Motion Artifacts in Brain MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- Data Leakage in Deep Learning for Alzheimer's Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer's disease (AD) and Parkinson's disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer's Disease Neuroimaging Initiative (ADNI), 48% on Parkinson's Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.